where \(\alpha \)  is the learning rate and \(b_{k}\)  is a baseline used
to reduce the variance of the policy gradient [1]}.
The entire training process is shown in Algorithm 4, using the REINFORCE
policy gradient algorithm [2]} to minimize the parameters
of the neural network and output an effective coflow scheduling strategy.
The key idea of the policy gradient algorithm is to estimate the gradient
using the trajectories of execution with the current policy.
